Cohere Transcribe is a state-of-the-art, 2B open-weights speech recognition model. Optimized for enterprise workloads, it delivers high throughput and a leading 5.42% WER across 14 languages, making it ideal for private, local, or desktop deployment.
Cohere just open-sourced Transcribe, and the core metrics here, especially the throughput and the 5.42% average WER, are genuinely impressive.
From an engineering point of view, this looks like a fantastic model for Mac/PC local apps or private enterprise servers. At 2B parameters, though, it still feels a bit heavy for raw on-device mobile deployment.
It is also worth noting that this is a highly optimized transcription engine, not a fully packaged meeting intelligence stack. Out of the box, you will still want to add your own layer for things like word-level timestamps and speaker diarization.
It also seems to perform best when you specify the language and avoid heavy code-switching.
But if you handle those pre- and post-processing steps and keep the audio mostly in a single language, this open-weight model looks extremely strong for privacy-first, local speech workflows.
Wow Zac! Was looking for something like this. What about the pricing? Is there a benchmark to compare with current competitors?
About Cohere Transcribe on Product Hunt
“New state-of-the-art in open source speech recognition”
Cohere Transcribe launched on Product Hunt on March 28th, 2026 and earned 136 upvotes and 6 comments, placing #6 on the daily leaderboard. Cohere Transcribe is a state-of-the-art, 2B open-weights speech recognition model. Optimized for enterprise workloads, it delivers high throughput and a leading 5.42% WER across 14 languages, making it ideal for private, local, or desktop deployment.
Cohere Transcribe was featured in Open Source (68.3k followers), Artificial Intelligence (466.2k followers) and Audio (2k followers) on Product Hunt. Together, these topics include over 100.8k products, making this a competitive space to launch in.
Who hunted Cohere Transcribe?
Cohere Transcribe was hunted by Zac Zuo. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Cohere Transcribe stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Zac Zuo
Hi everyone!
Cohere just open-sourced Transcribe, and the core metrics here, especially the throughput and the 5.42% average WER, are genuinely impressive.
From an engineering point of view, this looks like a fantastic model for Mac/PC local apps or private enterprise servers. At 2B parameters, though, it still feels a bit heavy for raw on-device mobile deployment.
It is also worth noting that this is a highly optimized transcription engine, not a fully packaged meeting intelligence stack. Out of the box, you will still want to add your own layer for things like word-level timestamps and speaker diarization.
It also seems to perform best when you specify the language and avoid heavy code-switching.
But if you handle those pre- and post-processing steps and keep the audio mostly in a single language, this open-weight model looks extremely strong for privacy-first, local speech workflows.